Asynchronous Objective Evaluation

The AsyncWrapperCOB class enables concurrent evaluation of a CombinedObjectiveFunction using Python’s asyncio framework.

It is particularly useful when objective terms internally perform blocking operations, for example:

  • subprocess.run() calls,

  • text-file-based simulations,

  • external command-line tools (e.g., LAMMPS, GROMACS),

  • other I/O-bound or external-code workloads.

This makes it an excellent companion to FileBasedQuantityComputer and related file-based workflows, where each term may trigger an external simulation.

Basic concept

A CombinedObjectiveFunction combines multiple sub-objectives into a single scalar loss. Evaluating all terms serially can be slow if each term runs a separate simulation or command-line tool.

AsyncWrapperCOB addresses this by:

  • wrapping a CombinedObjectiveFunction,

  • scheduling one async task per term,

  • running the blocking work for each term in a thread pool,

  • gathering the results concurrently,

  • summing all contributions into a single scalar loss.

Because it uses threads, no pickling is required, and it works well with file-based quantity computers and external simulators.

Example

Basic usage with a combined objective:

from concurrent.futures import ThreadPoolExecutor
from chemfit.combined_objective_function import CombinedObjectiveFunction
from chemfit.async_wrapper_cob import AsyncWrapperCOB
from chemfit.fitter import Fitter

# Suppose each term internally calls subprocess.run via file-based computers
combined = CombinedObjectiveFunction(objective_functions=terms)

# Optional: restrict concurrency via a custom thread pool
executor = ThreadPoolExecutor(max_workers=4)

# Wrap for async parallel evaluation
wrapper = AsyncWrapperCOB(combined, executor=executor)

fitter = Fitter(wrapper, initial_params={"x": 0.0})
opt_params = fitter.fit_scipy()

print(opt_params)

Integration with file-based quantity computers and external tools

A possible pattern in ChemFit is to use FileBasedQuantityComputer to drive an external simulation code (such as LAMMPS) via text-based input and output files:

  1. Parameters are mapped to an input file (e.g., a LAMMPS input script).

  2. A command is executed (often using subprocess.run()) to run the simulation.

  3. Output files are parsed into quantities through a user-defined parser.

  4. A loss function converts these quantities into a scalar objective value.

When many such file-based objectives are combined into a single CombinedObjectiveFunction, AsyncWrapperCOB allows these external simulations to be launched in parallel within a single Python process.

Sketch of usage with a LAMMPS-based workflow:

from concurrent.futures import ThreadPoolExecutor
from chemfit.file_based_computer import FileBasedQuantityComputer
from chemfit.abstract_objective_function import QuantityComputerObjectiveFunction
from chemfit.combined_objective_function import CombinedObjectiveFunction
from chemfit.async_wrapper_cob import AsyncWrapperCOB
from chemfit.fitter import Fitter

# Build several file-based computers, e.g. each running a different LAMMPS setup
energy_computer = FileBasedQuantityComputer(...)
density_computer = FileBasedQuantityComputer(...)
rdf_computer = FileBasedQuantityComputer(...)

energy_ob = QuantityComputerObjectiveFunction(
    loss_function=energy_loss_fn,
    quantity_computer=energy_computer,
)

density_ob = QuantityComputerObjectiveFunction(
    loss_function=density_loss_fn,
    quantity_computer=density_computer,
)

rdf_ob = QuantityComputerObjectiveFunction(
    loss_function=rdf_loss_fn,
    quantity_computer=rdf_computer,
)

combined = CombinedObjectiveFunction(
    objective_functions=[energy_ob, density_ob, rdf_ob],
    weights=[1.0, 0.5, 0.2],
)

# Use asyncio-based parallelism to run the LAMMPS-based terms concurrently
executor = ThreadPoolExecutor(max_workers=4)
async_wrapper = AsyncWrapperCOB(combined, executor=executor)

fitter = Fitter(async_wrapper, initial_params=initial_params)
opt_params = fitter.fit_scipy()

executor.shutdown(wait=True)

In this setting, each objective term may launch an independent LAMMPS run via a file-based quantity computer. AsyncWrapperCOB overlaps these runs in time, reducing total wall-clock time while keeping the API compatible with Fitter.

Comparison with MPI-based wrapper

AsyncWrapperCOB:

  • Uses threads on a single machine.

  • Requires no MPI installation.

  • Works naturally with file-based quantity computers and external tools like LAMMPS.

  • Ideal when you have a moderate number of independent, blocking terms.

MPIWrapperCOB:

  • Uses MPI across multiple processes or nodes.

  • Best suited for heavy simulations in distributed environments.

  • Requires MPI and mpi4py.

Choose the async wrapper when:

  • You are running on a single workstation or node.

  • Your objective terms are independent and blocking (e.g. LAMMPS runs).

  • You prefer a simpler, MPI-free parallelization layer.

Common pitfalls

  • Calling AsyncWrapperCOB.__call__ from within an already running event loop (e.g., some notebook or async frameworks) is not supported; in such contexts, use async_call() directly and await it.

  • Launching too many concurrent simulations may overload a single machine; use a custom ThreadPoolExecutor with a suitable max_workers limit.

  • Ensure that file-based workflows and external tools (e.g., LAMMPS input/output directories) are configured to avoid conflicting file names when running multiple simulations in parallel.

Summary

  • AsyncWrapperCOB provides async/threaded parallelism for objective-term evaluation.

  • It complements file-based quantity computers and is well suited to external tools like LAMMPS.

  • It is a drop-in wrapper around CombinedObjectiveFunction.

  • It offers a simple, MPI-free way to reduce wall-clock time when fitting against multiple, independent external simulations.